Poster Visuals

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library(tidyverse)

── Attaching core tidyverse packages ────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.2     ── Conflicts ──────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors

Visualizations for 2025 QCBS Poster Symposium.

Map with associated environmental data:

australia_map <- map_data("world", region = "Australia")
ggplot() +
  geom_polygon(data = australia_map, aes(x = long, y = lat, group = group), 
               fill = NA, color = "black", alpha = 0.5) +  # Transparent inside
  geom_point(data = aus_data, aes(x = long_deg, y = lat_deg), size = 4) +
  theme_void() +
  theme(
    panel.background = element_rect(fill = "transparent", colour = NA),
    plot.background = element_rect(fill = "transparent", colour = NA)
  )

#looks squished but will work

Species observations on map:

#species frequency table with associated location for plotting
australia_map <- map_data("world", region = "Australia")

species_geo <- tibble(
  species_binom = aus_data$species_binom,
  lat = aus_data$lat_deg,
  long = aus_data$long_deg) %>%
  group_by(species_binom) %>%
  mutate(frequency = n()) %>%
  ungroup() %>%
  select(species_binom, lat, long, frequency) %>%
  distinct() %>% #to remove duplicate rows
  arrange(desc(frequency))

ggplot() +
  geom_polygon(data = australia_map, aes(x = long, y = lat, group = group), 
               fill = "white", color = "black") +
  geom_point(data = subset(species_geo, frequency > 50), 
             aes(x = long, y = lat, color = species_binom), size = 2) +
  scale_color_discrete() +
  theme_classic()

ggplot() + australia_map +
  geom_point(data = subset(species_geo, frequency > 50), mapping = aes(x = long, y = lat,
             color = species_binom), size = 2) +
  scale_color_discrete() +
  theme_classic()

Warning: Incompatible methods ("+.gg", "Ops.data.frame") for "+"Error in ggplot() + australia_map : 
  non-numeric argument to binary operator

Leaf nutrient concentration by myc_type and fixation:

leaf_concentration_data <- aus_data %>%
  select(Unique_ID, woodiness, reclass_life_history, putative_BNF, myc_type,
         leaf_N_per_dry_mass, leaf_P_per_dry_mass, leaf_C_per_dry_mass,
         NP_ratio, CN_ratio, CP_ratio,
         ln_NP_ratio, ln_CN_ratio, ln_CP_ratio) %>%
  pivot_longer(cols = c(leaf_N_per_dry_mass, leaf_P_per_dry_mass, leaf_C_per_dry_mass,
                        NP_ratio, CN_ratio, CP_ratio,
                        ln_NP_ratio, ln_CN_ratio, ln_CP_ratio),
               names_to = "nutrient",
               values_to = "concentration") %>%
  mutate(nutrient = factor(case_when(
    nutrient == "leaf_N_per_dry_mass" ~ "N",
    nutrient == "leaf_P_per_dry_mass" ~ "P",
    nutrient == "leaf_C_per_dry_mass" ~ "C",
    nutrient == "NP_ratio" ~ "N:P Ratio", 
    nutrient == "CN_ratio" ~ "C:N Ratio",
    nutrient == "CP_ratio" ~ "C:P Ratio", 
    nutrient == "ln_NP_ratio" ~ "Logged N:P Ratio",
    nutrient == "ln_CN_ratio" ~ "Logged C:N Ratio",
    nutrient == "ln_CP_ratio" ~ "Logged C:P Ratio",
  )))

#ggplot(leaf_concentration_data, aes(x = factor(woodiness), y = concentration, fill = nutrient)) + 
 # geom_boxplot() +
  #scale_fill_manual(values = c("N" = "pink", "P" = "lightyellow", "C" = "lightgreen")) +
 # facet_wrap(~woodiness, scales = "free") +
  #theme_classic()

ggplot(subset(leaf_concentration_data, nutrient == "N"), 
       aes(x = factor(myc_type), y = concentration)) +
  geom_boxplot(fill = "pink") +
  #labs(x = "Woodiness", y = "Concentration (per dry mass)", 
      # title = "Leaf N by Woodiness")
  theme_classic()

ggplot(subset(leaf_concentration_data, nutrient == "Logged N:P Ratio"), 
       aes(x = factor(reclass_life_history), y = concentration)) +
  geom_boxplot(fill = "lightgrey") +
  #labs(x = "Woodiness", y = "Concentration (per dry mass)", 
      # title = "Leaf N by Woodiness")
  theme_classic()

NA
NA

Leaf nutrient concentration by species dot plot:

pruned <- prune_ausdata(aus_data, 62)
complete <- aus_data %>% mutate(species_binom = "All Species")
combined <- bind_rows(pruned, complete)

# Capitalize species_binom names
capitalize_species <- function(name) {
  name_parts <- strsplit(name, "_")[[1]]
  paste(toupper(substring(name_parts, 1, 1)), substring(name_parts, 2), sep = "", collapse = " ")
}

# Apply the capitalization function to species names in pruned dataframe
pruned <- pruned %>%
  mutate(species_binom = sapply(species_binom, capitalize_species))

# Correctly set the factor levels with "All Species" first
combined <- bind_rows(complete, pruned)
combined$species_binom <- factor(combined$species_binom,
                                 levels = c("All Species", unique(pruned$species_binom)))
#Explicitly define factor levels with "All Species" first


# Plot with adjustments
ggplot(combined, aes(x = species_binom, y = leaf_N_per_dry_mass)) +
  geom_jitter(width = 0.2, height = 0, size = 1.5, alpha = 0.6, color = "black") +
  theme_classic() +
  labs(title = "",
       y = "Leaf Nitrogen Concentration (per dry mass)",
       x = NULL) +
  theme(
    axis.text.x = element_text(angle = 50, hjust = 1, size = 20, color = "black"),
    axis.text.y = element_text(size = 14, color = "black"),
    axis.title.y = element_text(size = 20, color = "black", margin = margin(r = 20)),
    plot.title = element_text(size = 16, color = "black")
    )

Species-level lin. reg:

pruned <- prune_ausdata(aus_data, 62)

ggplot(pruned, aes(x = SN_total_0_30, y = (leaf_N_per_dry_mass), color = species_binom)) +
  geom_point(alpha = 0.6) +
  geom_smooth(method = "lm", se = FALSE)

  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

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Missing data densities:

na_data <- aus_data %>%
  mutate(across(c(leaf_P_per_dry_mass,leaf_C_per_dry_mass,
                  NP_ratio, CN_ratio, CP_ratio), as.character)) %>% 
  replace_na(list(leaf_N_per_dry_mass= "NA", leaf_P_per_dry_mass = "NA",
                  leaf_C_per_dry_mass = "NA",NP_ratio = "NA",
                  CN_ratio = "NA", CP_ratio = "NA"))

na_data <- na_data %>%
  filter(leaf_P_per_dry_mass == "NA" | leaf_C_per_dry_mass == "NA" |
           NP_ratio == "NA" | CN_ratio == "NA" | CP_ratio == "NA")

#specify colors
color_palette <- c("NA rows" = "lightgray", "All data" = "lightgreen")

#density plot of missing leaf concentration data
#using (complete) leaf N to compare rows with NAs for P and C
ggplot() +
  geom_density(data = aus_data,
               mapping = aes(x = leaf_N_per_dry_mass,
                             fill = "All data", alpha = 0.5)) +
  geom_density(data = na_data,
               mapping = aes(x = leaf_N_per_dry_mass,
                             fill = "NA rows", alpha = 0.5)) +
  labs(title = "Leaf N observations with Missing Leaf P and/or Leaf C",
       x = "leaf N", y = "Density") +
  theme_classic() +
  scale_fill_manual(values = color_palette, name = " ") +
  theme(legend.position = c(0.83, 0.86))

#missing env. coverage
ggplot() +
  geom_density(data = aus_data,
               mapping = aes(x = SN_total_0_30,
                             fill = "All data", alpha = 0.5)) +
  geom_density(data = na_data,
               mapping = aes(x = SN_total_0_30,
                             fill = "NA rows", alpha = 0.5)) +
  labs(title = "Observations with Missing Leaf P and/or Leaf C",
       y = "Density") +
  theme_classic() +
  scale_fill_manual(values = color_palette, name = " ") +
  theme(legend.position = c(0.83, 0.86))

Phylogenetic tree:

library(ggtree)
library(tidytree)
library(treeio)
setwd("/Users/sofiaquijada/Library/Mobile Documents/com~apple~CloudDocs/McGill/2024 Soper Lab/AusStoich-Collab")

Warning: The working directory was changed to /Users/sofiaquijada/Library/Mobile Documents/com~apple~CloudDocs/McGill/2024 Soper Lab/AusStoich-Collab inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

#works but only if all run at once
ausdata_all_pos_sp_tree <- read.tree("Inputs/Trees/ausdata_all_pos_sp.tre")
austraits_all_pos_sp_df <- read_csv('Inputs/all_pos_austraits_LCVP_sp.csv')

Rows: 829 Columns: 5── Column specification ─────────────────────────────────────────────────────────────────
Delimiter: ","
chr (3): species, genus, family
lgl (2): species.relative, genus.relative
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

all_pos_sp_data <- aus_data[aus_data$species_binom %in%
                   austraits_all_pos_sp_df$species, ]

all_pos_sp_data <- add_CV_columns(select_relevant_columns(all_pos_sp_data))

avg_all_pos_sp_data <- average_nutrient_data(all_pos_sp_data)

#horizontal base
all_pos_sp_plot <- ggtree(ausdata_all_pos_sp_tree) + geom_tiplab(size = 0.5)

#most basic, no coloring, horizontal bar plot
all_pos_sp_plot + geom_facet(
  panel = 'Trait',
  data = avg_all_pos_sp_data,
  geom = geom_col,
  mapping = aes(x = avg_leaf_N),
  orientation = "y") +
  ggtitle("") +
  theme(plot.title = element_text(size = 20))

names(avg_all_pos_sp_data)[1] <- "label"
attemptree <- full_join(as.treedata(ausdata_all_pos_sp_tree), avg_all_pos_sp_data, by = "label")

#mess with line thickness, and very small tip labels to manually write over later
#get label in the middle
ggtree(attemptree, aes(color = avg_leaf_N), layout = "circular") +
  scale_color_continuous(low = "#6ad1f3", high = "#ee6b00") +
  geom_tiplab(size = 0.5)